Description

Naive Bayes Classifier.

We support the multinomial Naive Bayes and multinomial Naive Bayes model, a probabilistic learning method. Here, feature values of train table must be nonnegative.

Details info of the algorithm: https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html

Parameters

Name Description Type Required？ Default Value
modelType model type : Multinomial or Bernoulli. String “Multinomial”
featureCols Names of the feature columns used for training in the input table String[] null
labelCol Name of the label column in the input table String ✓
weightCol Name of the column indicating weight String null
vectorCol Name of a vector column String null
smoothing the smoothing factor Double 1.0
vectorCol Name of a vector column String null
predictionCol Column name of prediction. String ✓
predictionDetailCol Column name of prediction result, it will include detailed info. String
reservedCols Names of the columns to be retained in the output table String[] null

Script Example

Script

  1. data = np.array([
  2.     [1.0, 1.0, 0.0, 1.0, 1],
  3.     [1.0, 0.0, 1.0, 1.0, 1],
  4.     [1.0, 0.0, 1.0, 1.0, 1],
  5.     [0.0, 1.0, 1.0, 0.0, 0],
  6.     [0.0, 1.0, 1.0, 0.0, 0],
  7.     [0.0, 1.0, 1.0, 0.0, 0],
  8.     [0.0, 1.0, 1.0, 0.0, 0],
  9.     [1.0, 1.0, 1.0, 1.0, 1],
  10.     [0.0, 1.0, 1.0, 0.0, 0]])
  11. df = pd.DataFrame({"f0": data[:, 0], 
  12.                    "f1": data[:, 1],
  13.                    "f2": data[:, 2],
  14.                    "f3": data[:, 3],
  15.                    "label": data[:, 4]})
  16. df["label"] = df["label"].astype('int')
  17. batchData = dataframeToOperator(df, schemaStr='f0 double, f1 double, f2 double, f3 double, label int', op_type='batch')
  19. # load data
  20. colnames = ["f0","f1","f2", "f3"]
  21. ns = NaiveBayes().setFeatureCols(colnames).setLabelCol("label").setPredictionCol("pred")
  22. model = ns.fit(batchData)
  23. model.transform(batchData).print()

Result

f0 f1 f2 f3 label pred
1.0 1.0 0.0 1.0 1 1
1.0 0.0 1.0 1.0 1 1
1.0 0.0 1.0 1.0 1 1
0.0 1.0 1.0 0.0 0 0
0.0 1.0 1.0 0.0 0 0
0.0 1.0 1.0 0.0 0 0
0.0 1.0 1.0 0.0 0 0
1.0 1.0 1.0 1.0 1 1
0.0 1.0 1.0 0.0 0 0